Automated Software Trust via SBOM Hierarchy Comparison
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Solution Overview
Problem
Software upgrades pose security risks due to potential introduction of unintended functionality, making it difficult to determine whether changes are acceptable in terms of security governance, certification, and threat modeling, especially with increasing automation leading to new exploit forms like dependency confusion attacks.
Innovation Solution
An automated approach that analyzes software packages using software bills of materials (SBOMs) to compare hierarchies of components and apply security policies based on the degree of difference, employing graph edit distance analysis and machine learning models to determine the risk of software updates and enforce security policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If software packages are frequently updated to improve functionality and security, then the software becomes more adaptive and secure, but the risk of introducing new vulnerabilities and security risks increases
Solution Approach 1:
The system performs preliminary analysis of software updates by comparing SBOMs before deployment. The automated system pre-evaluates proposed changes by computing graph edit distances between old and new software component hierarchies, identifying potential security issues before they can be exploited in production environments.
Solution Approach 2:
The system implements continuous feedback loops where software updates are monitored, analyzed, and evaluated after deployment. The automated comparison of SBOMs provides ongoing feedback about component changes, enabling security teams to respond to unexpected vulnerabilities and maintain security posture dynamically.
2Productivity
If automated processes are increased to improve productivity and speed of software deployment, then the efficiency improves, but new forms of exploits like dependency confusion attacks become more popular
Solution Approach 1:
The system introduces an automated intermediary layer between software package sources and deployment targets. This intermediary automatically verifies package provenance, compares SBOMs against known good configurations, and validates component hierarchies before allowing automated deployment, thereby preventing dependency confusion attacks while maintaining deployment speed.
Solution Approach 2:
The system replaces manual security review processes with automated computational analysis. Machine learning models and graph algorithms automatically analyze SBOMs, compute edit distances, and make security decisions, eliminating the need for manual intervention while maintaining or improving security posture against automated attacks.
3Reliability
If manual review of software package changes is performed to ensure security governance and certification compliance, then the security control improves, but the time and resources required increase significantly
Solution Approach 1:
The system enables security compliance to be self-verified through automated SBOM comparison and analysis. The system autonomously generates compliance reports, compares software component hierarchies against security policies, and certifies updates without requiring manual security reviewer intervention, thereby maintaining reliability while eliminating time losses.
4Measurement precision
If comprehensive analysis of software component hierarchies is performed to determine trustworthiness, then the security accuracy improves, but the computational complexity and analysis time increase
Solution Approach 1:
The system transforms the complex problem of software trust determination into measurable parameters such as graph edit distance metrics and component hierarchy similarity scores. By quantifying trustworthiness through computational parameters rather than qualitative assessment, the system achieves high measurement precision while managing complexity through mathematical models.
Data Source
AI summary
A method, computer system, and computer program product are provided for automatically analyzing software packages to identify the degree of differences between compared software packages and to apply security policies. A first software bill of materials for a software package is processed to extract a plurality of components of the software package, wherein the first software bill of materials indicates a first hierarchy of components based on relationships between components. The first hierarchy is compared to a second hierarchy, the second hierarchy corresponding to a second software bill of materials, to determine a degree of difference between the first hierarchy and the second hierarchy. The degree of difference is compared to one or more threshold values. A security policy is applied with respect to the software package according to a comparison of the degree of difference to the one or more threshold values.